class_docstr: Model-Agnostic Meta-Learning (MAML).
  
  Args:
      inner_algo (garage.torch.algos.VPG): The inner algorithm used for
          computing loss.
      env (Environment): An environment.
      policy (garage.torch.policies.Policy): Policy.
      sampler (garage.sampler.Sampler): Sampler.
      task_sampler (garage.experiment.TaskSampler): Task sampler.
      meta_optimizer (Union[torch.optim.Optimizer, tuple]):
          Type of optimizer.
          This can be an optimizer type such as `torch.optim.Adam` or a tuple
          of type and dictionary, where dictionary contains arguments to
          initialize the optimizer e.g. `(torch.optim.Adam, {'lr' : 1e-3})`.
      meta_batch_size (int): Number of tasks sampled per batch.
      inner_lr (float): Adaptation learning rate.
      outer_lr (float): Meta policy learning rate.
      num_grad_updates (int): Number of adaptation gradient steps.
      meta_evaluator (MetaEvaluator): A meta evaluator for meta-testing. If
          None, don't do meta-testing.
      evaluate_every_n_epochs (int): Do meta-testing every this epochs.
docstr: Return a policy used before adaptation to a specific task.
  
  Each time it is retrieved, this policy should only be evaluated in one
  task.
  
  Returns:
      Policy: The policy used to obtain samples that are later used for
          meta-RL adaptation.
docstr_tok: ["Return","a","policy","used","before","adaptation","to","a","specific","task",".","Each","time","it","is","retrieved",",","this","policy","should","only","be","evaluated","in","one","task",".","Returns",":","Policy",":","The","policy","used","to","obtain","samples","that","are","later","used","for","meta-RL","adaptation","."]
code: def get_exploration_policy(self):
      """Return a policy used before adaptation to a specific task.
  
      Each time it is retrieved, this policy should only be evaluated in one
      task.
  
      Returns:
          Policy: The policy used to obtain samples that are later used for
              meta-RL adaptation.
  
      """
      return copy.deepcopy(self._policy)
  
imports: import collections
  import copy
  from dowel import tabular
  import numpy
  import torch
  from garage import _Default, EpisodeBatch, log_multitask_performance, make_optimizer
  from garage.np import discount_cumsum
  from garage.torch import update_module_params
  from garage.torch._functions import np_to_torch, zero_optim_grads
  from garage.torch.optimizers import ConjugateGradientOptimizer, DifferentiableSGD
label_desc: Use image node_id 15 for calling the MAML obj's underlying member method code with example usage: obj.get_exploration_policy() and returns: copy